This article provides a beginner-level introduction to Natural Language Processing (NLP) and outlines some important techniques within the field for those who are unfamiliar with it.
Don’t worry, we won’t go into detail about the logic behind NLP, but rather try to outline why NLP might be a crucial component in the success and longevity of your business.
Introduction to NLP
NLP is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data like text or voice data.
NLP techniques are used to build machine learning models that can automatically read and understand natural language data, identify key topics and sentiment, extract named entities, and carry out many other tasks.
In this blog post, we will introduce some of the most popular NLP techniques and explain how you can leverage them to gain competitive advantage.
Why should you pay attention?
Because in recent years, NLP models have come to perform typical reading comprehension tasks at a human level, but at a fraction of the time.
NLP can be used to automate tasks, to improve customer service, and to better understand customers. NLP can help businesses to automate tasks by allowing them to develop algorithms that can parse and interpret natural language data. This can save businesses a lot of time and money as they will not need to hire as many employees to do these tasks manually.
By understanding the natural language of their customers, businesses can develop more effective customer service strategies. They can also develop automated customer service systems that can provide quick and accurate responses to customer queries.
Finally, NLP can help businesses to better understand their customers. By analyzing the natural language data of their customers, businesses can gain insights into their needs and wants. This information can then be used to develop more targeted marketing campaigns and products.
So now that you understand why you should leverage NLP, let’s go over some of the most common techniques!
Sentiment Analysis
Sentiment analysis is a process of computationally determining whether a piece of text is positive, negative, or neutral. It’s often used to gauge customers’ feelings about a product or service.
A sentiment analysis could be conducted as a marketing research effort. It could also be used to fast track angry customers.
Text Classification
Text classification is a process of assigning a label or category to a piece of text. This can be done manually, but is often done with algorithms that automatically learn to classify text from a set of labeled examples.
There are many different applications for text classification, such as spam filtering, topic classification, and document categorization.
In general, text classification is a very useful tool for automatically understanding and organizing large amounts of text data. A common use case is ticket routing, where an incoming customer support ticket is routed to the right department based on the content of the message.
Named Entity Recognition
Named entity recognition (NER) is a sub-task of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
A common use case for NER is masking sensitive information, such as social security numbers to assure that you're complying to legislative requirements, for example GDPR.
Topic Modeling
Topic modeling is a NLP method that finds hidden patterns in large collections of text by tagging or grouping the texts into clusters. It's a useful technique for exploring the topics underlying a collection of documents.
Text Summarization
Text summarization is the process of reducing a text document to its most important points. The purpose of summarization is to create a shorter, more readable version of a text that retains its original meaning.
The final summary should be clear and concise, and should accurately represent the main ideas of the original text. Summaries can be written in different styles, depending on their purpose. For example, an objective summary would simply state the main points of the text, without adding any interpretation or personal opinion.
